The proposed two stage deep learning model, shown in Fig. REF , consists of two consecutive capsule networks [1]}, [2]}, which are advantageous over commonly used convolutional neural networks (CNNs) in handling the spatial relations between image instances. The segmented chest CT scans are the inputs to the first stage, which identifies images with evidence of infection. The slice with infection could be related to a CAP or COVID-19 patient. 10 most probable slices with infection are then selected as inputs to the second stage, which consists of time-distributed capsule networks, referring to processing slices at the same time through the same model. In this stage, classification probabilities generated from individual slices go through a global max pooling operation to make the final decision. Next, the two stages are explained in more details.
